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Record W2528278659 · doi:10.1200/jop.2016.013797

Addressing Opioid-Associated Constipation Using Quality Oncology Practice Initiative Scores and Plan-Do-Study-Act Cycles

2016· article· en· W2528278659 on OpenAlexaff
Varinder Kaur, Sajjad Haider, Appalanaidu Sasapu, Paulette Mehta, Konstantinos Arnaoutakis, Issam Makhoul

Bibliographic record

VenueJournal of Oncology Practice · 2016
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsMedicineAuditQuality managementFamily medicineConstipationMedical educationInternal medicineManagement system

Abstract

fetched live from OpenAlex

Using the Quality Oncology Practice Initiative, an affiliate program of ASCO, we outlined opioid-associated constipation (OAC) as a subject in need of quality improvement (QI) in our fellowship program at the University of Arkansas for Medical Sciences and Central Arkansas Veterans Healthcare System. We initiated a fellow-led QI project to advance the quality of patient care and provide a valuable avenue for QI training of young physicians. Fellows organized meetings with all stakeholders, addressed the scope of the problem, and devised strategies for OAC management. Monthly meetings were organized using Plan-Do-Study-Act principles. Mandatory check boxes were inserted into our electronic medical record templates to remind all physicians to identify patients on opioid medications and assess and address OAC. Final chart audit and patient satisfaction surveys were performed 6 months after project initiation. Assessment of OAC improved from 52% at baseline to 92% ( P < .003). This improvement corresponded with high patient satisfaction scores, with 90% of surveyed patients reporting adequate management of their constipation. In this QI initiative, we showed that participation in ASCO's Quality Oncology Practice Initiative helps identify areas in need of QI, and such fellow-led QI projects can serve as models for QI training of young physicians.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.092
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.092
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.263
GPT teacher head0.492
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2016
Admission routes1
Has abstractyes

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